Automated Detection of Gastrointestinal Diseases Using Resnet50*-Based Explainable Deep Feature Engineering Model with Endoscopy Images by Veysel Yusuf Cambay, Prabal Datta Barua, Abdul Hafeez Baig, Sengul Dogan, Mehmet Baygin, Turker Tuncer, U. R. Acharya

“…This work aims to develop a novel convolutional neural network (CNN) named ResNet50* to detect various gastrointestinal diseases using a new ResNet50*-based deep feature engineering model with endoscopy images. …”
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Construction of a predictive model for the efficacy of anti-VEGF therapy in macular edema patients based on OCT imaging: a retrospective study by Tingting Song, Boyang Zang, Chui Kong, Xifang Zhang, Huihui Luo, Wenbin Wei, Zheqing Li

“…This model innovatively introduces group convolution and multiple convolutional kernels to handle multidimensional features based on traditional attention mechanisms for visual recognition tasks, while utilizing spatial pyramid pooling (SPP) to combine and extract the most useful features. …”
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Laryngeal cancer diagnosis based on improved YOLOv8 algorithm by Xin Nie, Xueyan Zhang, Di Wang, Yuankun Liu, Lumin Xing, Wenjian Liu

“…Laryngeal cancer is the most common malignant tumor in the head and neck region. …”
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Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data by Bas Peters, Eldad Haber, Keegan Lensink

“…While reversibility saves the major amount of memory used in deep networks by the data, the convolutional kernels can take up most memory if fully invertible networks contain multiple invertible pooling/coarsening layers. …”
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Vessel Trajectory Prediction Method Based on the Time Series Data Fusion Model by Xinyun WU, Jiafei CHEN, Caiquan XIONG, Donghua LIU, Xiang WAN, Zexi CHEN

“…To address this issue, this study introduces a method consisting of temporal convolutional network (TCN), convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM) to predict vessel trajectories, called TCC. …”
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Novel feature extraction method for signal analysis based on independent component analysis and wavelet transform. by Mariusz Topolski, Jędrzej Kozal

“…Compared to other literature methods, our approach was better than most feature extraction methods except for convolutional neural networks. …”
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A Lightweight Semantic- and Graph-Guided Network for Advanced Optical Remote Sensing Image Salient Object Detection by Jie Liu, Jinpeng He, Huaixin Chen, Ruoyu Yang, Ying Huang

“…This module incorporates non-local operations under graph convolution domain to deeply explore high-order relationships between adjacent layers, while utilizing depth-wise separable convolution blocks to significantly reduce computational cost. …”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…In recent years, spatiotemporal sequence prediction models based on deep learning have garnered significant attention and achieved notable progress in radar echo extrapolation. However, most of these extrapolation network architectures are built upon convolutional neural networks, using radar echo images as input. …”
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An analytical examination of the performance assessment of CNN-LSTM architectures for state-of-health evaluation of lithium-ion batteries by Arun Jose, Sonam Shrivastava

“…In this study, an assessment was conducted on various Convolutional Neural Network-Long Short-Term Memory architectures to explore their complete potential, with the generic architecture yielding the most favorable outcomes. …”
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Using Deep Learning to Predict Complex Systems: A Case Study in Wind Farm Generation by J. M. Torres, R. M. Aguilar

“…We also conduct a sensitivity analysis to determine which estimator type is most robust to perturbations. An analysis of our findings shows that the most accurate and robust estimators are those based on feedforward neural networks with a SELU activation function and convolutional neural networks.…”
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A Novel High Performance Object Identification Approach in Care Homes Using Gaussian Preprocessing by Sara Auweiler, Melina Mueller, Diana Puhla, Pascal Penava, Ricardo Buettner

“…Therefore we compare the performance of four well-known Convolutional Neural Network architectures — VGG16, VGG19, InceptionV3, and ResNet50 — to identify the most effective model for this application. …”
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A swin transformer and CNN fusion framework for accurate Parkinson disease classification in MRI by Sayyed Shahid Hussain, Pir Masoom Shah, Hussain Dawood, Xu Degang, Ahmad Alshamayleh, Muhammad Adnan Khan, Taher M. Ghazal

“…Abstract Parkinson’s disease ranks as the second most prevalent neurological disorder after Alzheimer’s disease. …”
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Pengembangan Deep Learning untuk Sistem Deteksi Dini Komplikasi Kaki Diabetik Menggunakan Citra Termogram by Medycha Emhandyksa, Indah Soesanti, Rina Susilowati

“…Pada penelitian ini dirancang empat model deep convolutional neural network dengan prinsip Occam’s razor melalui pengaturan hyperparameter pada aspek struktur algoritma berupa jumah layer dan aspek optimasi berupa tipe optimizer. …”
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Comparisons of different deep learning-based methods on fault diagnosis for geared system by Bing Han, Xiaohui Yang, Yafeng Ren, Wanggui Lan

“…Based on the measured gear fault vibration signals and the deep learning theory, four fault diagnosis neural network models including fast Fourier transform–deep belief network model, wavelet transform–convolutional neural network model, Hilbert-Huang transform–convolutional neural network model, and comprehensive deep neural network model are developed and trained respectively. …”
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A Rolling-Bearing-Fault Diagnosis Method Based on a Dual Multi-Scale Mechanism Applicable to Noisy-Variable Operating Conditions by Jing Kang, Taiyong Wang, Ye Wei, Usman Haladu Garba, Ying Tian

“…This approach targets the intrinsic mode functions (IMFs), which capture information across multiple scales, to obtain the most precise denoised signal possible. Subsequently, we introduce the Dynamic Weighted Multi-Scale Feature Convolutional Neural Network (DWMFCNN) model, which integrates two structures: multi-scale feature extraction and dynamic weighting of these features. …”
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A novel multi-scale and fine-grained network for large choroidal vessels segmentation in OCT by Wei Huang, Qifeng Yan, Lei Mou, Yitian Zhao, Wei Chen, Wei Chen, Wei Chen

“…The experimental results show that the proposed method has the best performance compared to the most advanced segmentation networks currently available. …”
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Quantum‐inspired Arecanut X‐ray image classification using transfer learning by Praveen M. Naik, Bhawana Rudra

“…A comparative analysis of transfer learning‐based classification, employing both a traditional convolutional neural network (CNN) and an advanced quantum convolutional neural network (QCNN) approach is conducted. …”
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YOLO-WTB: Improved YOLOv12n Model for Detecting Small Damage of Wind Turbine Blades From Aerial Imagery by Phat T. Nguyen, Duy C. Huynh, Loc D. Ho, Matthew W. Dunnigan

“…In addition, in the backbone part, we also propose to remove a Convolution module and an Area Attention Concatenate-Convolution-Fusion module and add an improved SoftPool Feature Spatial Pyramid Pooling - Fast module to increase the feature extraction ability while maintaining the complexity of the model. …”
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Partial feature reparameterization and shallow-level interaction for remote sensing object detection by Minh Tai Pham Nguyen, Quoc Duy Nam Nguyen, Hoang Viet Anh Le, Minh Khue Phan Tran, Tadashi Nakano, Thi Hong Tran

“…Firstly, an extraction block is proposed called PRepConvBlock that leverages reparameterization convolution and partial feature utilization to effectively reduce the complexity in convolution operations, allowing for the utilization of larger kernel sizes in order to form the longer interactions between features and significantly expand receptive fields. …”
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Evaluation of machine learning and deep learning algorithms for fire prediction in Southeast Asia by Aditya Eaturu, Krishna Prasad Vadrevu

“…In this study, we utilize Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-derived fire data alongside six machine learning (ML) and deep learning (DL) models, Simple Persistence, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-Long Short-Term Memory (CNN-LSTM), and Convolutional Long Short-Term Memory (ConvLSTM) to determine the most effective fire prediction model. …”
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